Image generation algorithms generally fail when input images are distorted by huge noise, for example, lens rain drop distortion, blur, or other obscurities. By way of an example, video surveillance system may fail if a captured video is highly distorted because of rain drop noise on the lens of a camera. This leads to failure of the system in real time usage.
Camera based imaging systems are commonly used in vehicles (for example, dash cameras). With advancement of processor technology and availability of algorithms for Advanced Driver Assistive System (ADAS) and self-driving cars, these imaging systems have become more and more relevant. In ADAS and self-driving cars, the role of these imaging system have been extended from assistance to decision making, which demands for greater reliability and accuracy.
However, imaging system in vehicles are generally exposed to outside environment, for example, rain drops, dirt, or mud, resulting in partial or complete occlusion or distortion of the captured images. Convention computer vision algorithms fail to perform well if the input image is distorted because of exposure to the outside environment. This leads to reduced accuracy or even failure of decision making system in vehicles.